253 lines
9.8 KiB
Python
253 lines
9.8 KiB
Python
"""
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main.py
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智能体 Demo 程序入口（OpenAI Function Calling 驱动）
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运行模式:
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python main.py → 交互模式
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python main.py demo → 演示模式
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python main.py config → 打印当前配置
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python main.py health → 检测 OpenAI API 连通性
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LLM_API_KEY=sk-xxx python main.py → 指定 API Key
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LLM_MODEL_NAME=gpt-4-turbo python main.py→ 指定模型
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AGENT_CONFIG_PATH=my.yaml python main.py → 指定配置文件
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"""
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import sys
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from client.agent_client import AgentClient, AgentResponse
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from config.settings import settings
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from llm.llm_engine import LLMEngine
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from mcp.mcp_server import MCPServer
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from memory.memory_store import MemoryStore
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from tools.calculator import CalculatorTool
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from tools.code_executor import CodeExecutorTool
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from tools.file_reader import FileReaderTool
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from tools.web_search import WebSearchTool
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from utils.logger import get_logger
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logger = get_logger("SYSTEM")
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_ALL_TOOLS = {
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"calculator": CalculatorTool,
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"web_search": WebSearchTool,
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"file_reader": FileReaderTool,
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"code_executor": CodeExecutorTool,
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}
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# ── 系统组装 ───────────────────────────────────────────────────
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def build_agent() -> AgentClient:
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"""工厂函数：由 settings 驱动的 Agent 组装"""
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logger.info("🔧 开始组装 Agent 系统（OpenAI Function Calling 模式）...")
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logger.info(settings.display())
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mcp_server = MCPServer()
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for tool_cls in _ALL_TOOLS.values():
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mcp_server.register_tool(tool_cls)
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llm = LLMEngine()
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memory = MemoryStore(max_history=settings.memory.max_history)
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client = AgentClient(llm=llm, mcp_server=mcp_server, memory=memory)
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logger.info(f"✅ Agent 组装完成，已注册工具: {mcp_server.list_tools()}")
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return client
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# ── 结果打印 ───────────────────────────────────────────────────
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def print_response(response: AgentResponse) -> None:
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"""格式化打印 AgentResponse"""
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print(f"\n{'═' * 62}")
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print(f"👤 用户: {response.user_input}")
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print(f"{'─' * 62}")
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if response.chain_result:
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cr = response.chain_result
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tag = "🔗 多步串行" if response.is_multi_step else "🔧 单步调用"
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status = "✅ 全部成功" if cr.success else f"⚠️ 步骤 {cr.failed_step} 失败"
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print(f"{tag} | {cr.completed_steps}/{cr.total_steps} 步 | {status}")
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print()
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for r in cr.step_results:
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icon = "✅" if r.success else "❌"
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preview = r.output.replace("\n", " ")[:90]
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print(f" {icon} Step {r.step_id} [{r.tool_name}]")
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if r.success:
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print(f" └─ {preview}...")
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else:
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print(f" └─ 错误: {r.error}")
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print()
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print(f"🤖 Agent 回复:\n{response.final_reply}")
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print(f"{'═' * 62}\n")
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# ── API 健康检测 ───────────────────────────────────────────────
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def run_health_check() -> None:
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"""检测 OpenAI API 连通性"""
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print(f"\n{'─' * 50}")
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print(f" 🏥 OpenAI API 健康检测")
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print(f"{'─' * 50}")
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print(f" Provider : {settings.llm.provider}")
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print(f" Model : {settings.llm.model_name}")
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print(f" API Key : {'***' + settings.llm.api_key[-4:] if len(settings.llm.api_key) > 4 else '(未设置)'}")
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print(f" Base URL : {settings.llm.api_base_url or 'https://api.openai.com/v1'}")
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print(f"{'─' * 50}")
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if not settings.llm.api_key:
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print(" ❌ API Key 未设置")
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print(" 💡 请设置环境变量: export LLM_API_KEY=sk-...")
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print(f"{'─' * 50}\n")
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return
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print(" ⏳ 正在检测连通性...")
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llm = LLMEngine()
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ok = llm.provider.health_check()
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if ok:
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print(f" ✅ API 连通正常，模型 [{settings.llm.model_name}] 可用")
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else:
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print(f" ❌ API 连接失败，请检查网络或 API Key")
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print(f" 💡 可尝试设置代理: export LLM_API_BASE_URL=https://your-proxy/v1")
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print(f"{'─' * 50}\n")
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# ── 演示场景 ───────────────────────────────────────────────────
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def run_demo(client: AgentClient) -> None:
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"""运行预设演示场景"""
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demo_cases = [
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("🔢 单步: 数学计算",
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"计算 (100 + 200) × 3 等于多少？"),
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("🌐 单步: 网络搜索",
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"搜索 Python 3.12 的主要新特性"),
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("🔗 两步: 搜索 + 计算",
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"搜索 Python 最新版本号，然后计算 3.12 × 100 的结果"),
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("🔗 两步: 读取文件 + 执行代码",
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"读取 script.py 文件然后执行里面的代码"),
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("💬 无工具: 直接问答",
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"你好，请介绍一下你自己"),
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]
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logger.info("\n" + "═" * 62)
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logger.info(f"🎬 演示模式 | 模型: {settings.llm.model_name} | "
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f"Provider: {settings.llm.provider}")
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logger.info("═" * 62)
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for title, question in demo_cases:
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logger.info(f"\n📌 场景: {title}")
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response = client.chat(question)
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print_response(response)
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stats = client.get_memory_stats()
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print(f"📊 Memory 统计: {stats}\n")
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# ── 交互模式 ───────────────────────────────────────────────────
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def run_interactive(client: AgentClient) -> None:
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"""启动交互式命令行对话"""
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print("\n" + "═" * 62)
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print(f" 🤖 Agent | {settings.llm.model_name} | {settings.llm.provider}")
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print(f" Function Calling: {'✅ 开启' if settings.llm.function_calling else '❌ 关闭（规则引擎）'}")
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print(f" Fallback Rules : {'✅ 开启' if settings.agent.fallback_to_rules else '❌ 关闭'}")
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print("─" * 62)
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print(" 💡 示例:")
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print(" 计算 (100+200) × 3")
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print(" 搜索 Python 新特性，然后计算 3.12 × 100")
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print(" 读取 config.json 文件然后执行代码")
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print("─" * 62)
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print(" 🛠 命令: config / health / tools / chains / stats / clear / quit")
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print("═" * 62 + "\n")
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while True:
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try:
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user_input = input("👤 你: ").strip()
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except (KeyboardInterrupt, EOFError):
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print("\n👋 再见！")
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break
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if not user_input:
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continue
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match user_input.lower():
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case "quit" | "exit":
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print("👋 再见！")
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break
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case "config":
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print(settings.display())
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case "health":
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run_health_check()
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case "clear":
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client.clear_session()
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print("✅ 会话已清空\n")
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case "stats":
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print(f"📊 {client.get_memory_stats()}\n")
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case "tools":
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schemas = client.mcp_server.get_tool_schemas()
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print(f"🔧 已注册工具 ({len(schemas)} 个):")
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for s in schemas:
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print(f" • [{s.name}] {s.description}")
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print()
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case "chains":
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chains = client.memory.get_chain_history()
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if not chains:
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print("🔗 暂无调用链历史\n")
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else:
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print(f"🔗 调用链历史 ({len(chains)} 条):")
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for i, c in enumerate(chains, 1):
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steps = " → ".join(s["tool_name"] for s in c["steps"])
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ok_cnt = sum(1 for s in c["steps"] if s["success"])
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total = len(c["steps"])
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print(f" {i}. [{c['timestamp'][11:19]}] {c['goal'][:38]}...")
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print(f" 链路: {steps} ({ok_cnt}/{total} 步成功)")
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print()
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case _:
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response = client.chat(user_input)
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print_response(response)
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# ── 配置打印 ───────────────────────────────────────────────────
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def run_show_config() -> None:
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print(settings.display())
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print("\n📁 配置文件查找路径（按优先级）:")
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print(" 1. 环境变量 AGENT_CONFIG_PATH")
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print(" 2. ./config/config.yaml")
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print(" 3. ./config.yaml")
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print("\n🌍 支持的环境变量覆盖:")
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env_vars = [
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("LLM_API_KEY", "OpenAI API 密钥（sk-...）"),
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("LLM_MODEL_NAME", "模型名称，如 gpt-4o / gpt-4-turbo"),
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("LLM_API_BASE_URL", "自定义 API 地址（兼容代理）"),
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("LLM_MODEL_PATH", "本地模型路径"),
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("SEARCH_API_KEY", "搜索 API 密钥"),
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("LOG_LEVEL", "日志级别 DEBUG/INFO/WARNING/ERROR"),
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("AGENT_CONFIG_PATH","配置文件路径"),
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]
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for var, desc in env_vars:
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print(f" {var:<22} → {desc}")
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print()
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# ── 主函数 ─────────────────────────────────────────────────────
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def main() -> None:
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mode = sys.argv[1] if len(sys.argv) > 1 else "interactive"
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if mode == "config":
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run_show_config()
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return
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if mode == "health":
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run_health_check()
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return
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client = build_agent()
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if mode == "demo":
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run_demo(client)
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else:
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run_interactive(client)
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if __name__ == "__main__":
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main() |